Mitigation of Residential EV Charging Effects on Power Distribution Networks with Optimal Allocation of DGs Using Coati Optimization

Electric vehicle (EV) charging deteriorates the performance of power distribution networks. This may lead to issues such as distortion of the daily demand profile, deterioration in bus voltage magnitudes, increased system losses, phase imbalances and power quality concerns. This paper examines the effects of slow, residential EV charging on a 240-bus power distribution network in Iowa, U.S.A. The investigation estimates the charging demand of approximately 1,500 EVs across four different segments, representing over 1,000 U.S. residents, using data from the 2017 National Household Travel Survey. The findings reveal that slow, residential EV charging significantly increases network load, particularly during or after evening hours. To counteract these adverse effects, wind power-based distributed generation (DG) resources were optimally allocated in the 240-bus network using the coati optimization algorithm (COA), which is a recently developed metaheuristic algorithm. A multi-objective optimization problem was formulated to minimize both active power losses and the voltage magnitude deviation of the network. Wind speed patterns for the site were sourced from Iowa Environmental Mesonet data. The COA simplifies implementation, reduces complexity and scales efficiently, making it ideal for real-world power systems. It outperforms traditional methods by finding optimal solutions faster while minimizing computational time and resources for DG sizing. The results demonstrate that the proposed technique effectively reduces network active power losses by 38.45% and network voltage deviation by 51.61%, validating its ability to mitigate the negative impacts of EV charging demand on the power distribution network performance.